# Towards Automatic Power Battery Detection: New Challenge, Benchmark Dataset and Baseline

arXiv: https://arxiv.org/abs/2312.02528 · HTML: https://mlsanity.com/paper/2312.02528/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 57 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid](https://mlsanity.com/paper/1805.06115/) (2018)
- [Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks](https://mlsanity.com/paper/2210.13826/) (2022)

## Built on by (most cited first, 5 of 5)

- [Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes](https://mlsanity.com/paper/2412.01240/) (2024)
- [Rethinking Evaluation of Infrared Small Target Detection](https://mlsanity.com/paper/2509.16888/) (2025)
- [UniMRSeg: Unified Modality-Relax Segmentation via Hierarchical Self-Supervised Compensation](https://mlsanity.com/paper/2509.16170/) (2025)
- [Power Battery Detection](https://mlsanity.com/paper/2508.07797/) (2025)
- [Pattern-Enhanced RT-DETR for Multi-Class Battery Detection](https://mlsanity.com/paper/2605.13670/) (2026)

## Abstract (arXiv)

We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries. Existing manufacturers usually rely on human eye observation to complete PBD, which makes it difficult to balance the accuracy and efficiency of detection. To address this issue and drive more attention into this meaningful task, we first elaborately collect a dataset, called X-ray PBD, which has 1,500 diverse X-ray images selected from thousands of power batteries of 5 manufacturers, with 7 different visual interference. Then, we propose a novel segmentation-based solution for PBD, termed multi-dimensional collaborative network (MDCNet). With the help of line and counting predictors, the representation of the point segmentation branch can be improved at both semantic and detail aspects.Besides, we design an effective distance-adaptive mask generation strategy, which can alleviate the visual challenge caused by the inconsistent distribution density of plates to provide MDCNet with stable supervision. Without any bells and whistles, our segmentation-based MDCNet consistently outperforms various other corner detection, crowd counting and general/tiny object detection-based solutions, making it a strong baseline that can help facilitate future research in PBD. Finally, we share some potential difficulties and works for future researches. The source code and datasets will be publicly available at hrefhttps://github.com/Xiaoqi-Zhao-DLUT/X-ray-PBDX-ray PBD.